PhysioNet: A Resource for Research and Education

The PhysioNet Resource, established in 1999, is intended to stimulate
current research and new investigations in the study of complex biomedical
and physiologic signals. It has three closely interdependent components:

PhysioBank is a
large and growing archive of well-characterized digital recordings of
physiologic signals, time series, and related data for use by the biomedical
research community.

PhysioBank includes collections of
cardiopulmonary, neural, and other biomedical signals
from healthy subjects and patients with a variety of conditions with
major public health implications, including sudden cardiac death,
congestive heart failure, epilepsy, gait disorders, sleep apnea, and
aging. These collections include data from a wide range of studies,
as developed and contributed by members of the research community.

PhysioToolkit is a large
and growing library of software for physiologic signal processing and analysis,
detection of physiologically significant events using both classical techniques
and novel methods based on statistical physics and nonlinear dynamics,
interactive display and characterization of signals, creation of new databases,
simulation of physiologic and other signals, quantitative evaluation and
comparison of analysis methods, and analysis of nonequilibrium and
nonstationary processes.

A unifying theme of many of the research projects that
contribute software to PhysioToolkit is the extraction of “hidden”
information from biomedical signals, information that may have diagnostic or
prognostic value in medicine, or explanatory or predictive power in basic
research.

PhysioNetWorks is a virtual laboratory
where you can work together with us and with colleagues anywhere in the world
to create, evaluate, improve, document, and prepare new data and software
“works” for publication on PhysioNet.

Unlike all other parts of the PhysioNet
web site, access to PhysioNetWorks is password-protected. (Accounts can be obtained in a minute or two.) PhysioNet Works provides
reliable and secure web-accessible backup, tools for viewing and annotating
your data interactively, and an active community of more than 3000 researchers
around the world who can help you by annotating, analyzing, and reviewing your
data, and perhaps by contributing additional relevant data. To learn more,
start here.

PhysioNet is not only the name of
the Resource, but also of its web site, physionet.org. The PhysioNet web site
was established by the Resource as its mechanism for free and open
dissemination and exchange of recorded biomedical signals and open-source
software for analyzing them, by providing facilities for cooperative analysis
of data and evaluation of proposed new algorithms. In addition to providing
free electronic access to PhysioBank data and PhysioToolkit software, and
secure workspaces for collaborative development of new data and software within
PhysioNetWorks, the PhysioNet web site offers service and training via on-line
tutorials to assist users at entry and more advanced levels. In cooperation
with the annual Computing in
Cardiology conference, PhysioNet hosts an annual series
of challenges, in which researchers and students
address unsolved problems of clinical or basic scientific interest using data
and software provided by PhysioNet.

All data included in PhysioBank, and all software included in PhysioToolkit,
are carefully reviewed. We invite you to participate in the ongoing review
process. By sharing common data sets, and software in source form, the
research community benefits from access to materials that have been rigorously
scrutinized by many investigators. We further invite researchers to contribute
data and software via PhysioNetWorks for review and possible inclusion in
PhysioBank and PhysioToolkit. Please review our
guidelines for contributors before submitting
material.

Links to a variety of other on-line resources likely to be of interest to
PhysioNet visitors are listed here.

A Brief History

Beginning in the mid-1970s, members of the PhysioNet team who were then
working on some of the first microcomputer-based instruments for cardiac
arrhythmia monitoring foresaw the usefulness of establishing shared
databases of well-characterized ECG recordings, as a basis for evaluation,
iterative improvement, and objective comparison of algorithms for automated
arrhythmia analysis. A five-year effort culminated in the publication of
the MIT-BIH Arrhythmia Database in 1980, which soon became the standard
reference collection of its type, used by over 500 academic, hospital, and
industry researchers and developers worldwide during the 1980s and 1990s.
Other databases of ECGs and eventually other physiologic signals followed.
By 1999, the MIT group distributed CD-ROMs containing 11 such collections,
and had participated in the development of several others.

PhysioNet was established in 1999 as the outreach component of
the Research Resource for Complex Physiologic Signals, a
cooperative project initiated by a diverse group of computer
scientists, physicists, mathematicians, biomedical researchers,
clinicians, and educators at
Boston's Beth Israel Deaconess Medical
Center/Harvard
Medical School, Boston
University, and McGill University, all working together with the
MIT group. Many of us
have worked together for 20 years or even longer on problems relating
to characterizing and understanding the dynamics of human physiology,
the implications of dynamical change in diagnosis and treatment of
pathophysiology, novel and robust techniques for physiologic
monitoring in ambulatory subjects and critical care patients, and
applications of model-based reasoning to medical decision support in
intensive care. The MIT group contributed its 11 databases, and the
software it had developed for exploring and analyzing them, to
establish PhysioBank and PhysioToolkit. Free availability of these
resources via the Internet catalyzed an even greater explosion of
interest in them, as researchers and students worldwide who had no
previous access to such data or software began new programs of
research, and specialists began comparing their methods. These initial
contributions were quickly supplemented by additional collections of
data and software from their collaborators, and soon after, from many
researchers worldwide. PhysioBank and PhysioToolkit have grown to
many times their original sizes, and most of the growth has been
thanks to the hard work and generosity of an
international community of researchers.

At the time PhysioNet was established, members of the PhysioNet team
at MIT were preparing to host Computers in Cardiology 2000. We hoped
to introduce PhysioNet to our international colleagues who would be
attending CinC, by encouraging participation in an activity that made
effective use of the facilities provided by PhysioNet to stimulate
rapid progress on an unsolved problem of practical clinical
significance. A timely contribution of data made it possible to
create the first PhysioNet/CinC Challenge,
which attracted the attention of more than a dozen teams to the
subject of detecting sleep apnea from the ECG. Their efforts were
broadly successful, they discussed their findings at CinC 2000, and an
annual tradition was born.

Current Research within this Resource

Methods for assessment of signal quality and detection of events
in weakly correlated multiparameter data; false alarm reduction in the ICU;
methods for multivariate trend analysis and forecasting, with
applications in intensive care; cardiovascular system modeling (including
adaptation to microgravity and orthostatic intolerance);
novel signal processing techniques for automated or semi-automated patient
diagnosis; web-enabled signal processing, with applications in research and
telemedicine; data mining algorithms for efficient searching in very long time
series; networked instrumentation for acquisition and remote viewing of
real-time physiologic data
(Roger Mark,
George Moody,
Li-wei Lehman,
Ikaro Silva).

Algorithms that quantify the transient and local properties
of nonstationary physiologic signals and the cross-interactions among
multiparameter signals; application of these techniques to detect changes that
may precede the onset of catastrophic physiologic events, including epileptic
seizures and sudden cardiac death;
techniques for quantifying the dynamics of physiologic control;
mathematical/physiological modeling of these control mechanisms; identification
of new measures related to nonlinear dynamics and fractal scaling that have
diagnostic/prognostic use in life-threatening cardiopulmonary pathologies
(Madalena Costa,
Leon Glass,
Ary Goldberger,
Jeff Hausdorff,
CK Peng).

A list of recent publications by investigators affiliated with the Resource
is available here. Also
see the PhysioNet Contributors Page.